Modelling sentiment and trader aggressiveness in cryptocurrency markets: An empirical analysis of the Bitcoin limit order book.

Cryptocurrency assets are inherently susceptible to social sentiment. Due to emotional contagion, extreme emotions influence and drive traders’ order execution behaviour. This study aims to empirically model the relationship between sentiment and trader aggressiveness for two types of traders: Whale...

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Main Author: THANG, Angelique Nicole Huei Yin
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Language:English
Published: Institutional Knowledge at Singapore Management University 2023
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Online Access:https://ink.library.smu.edu.sg/etd_coll/541
https://ink.library.smu.edu.sg/context/etd_coll/article/1539/viewcontent/GPBA_AY2019_DBA_AngeliqueNicoleThang.pdf
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spelling sg-smu-ink.etd_coll-15392024-02-14T06:33:18Z Modelling sentiment and trader aggressiveness in cryptocurrency markets: An empirical analysis of the Bitcoin limit order book. THANG, Angelique Nicole Huei Yin Cryptocurrency assets are inherently susceptible to social sentiment. Due to emotional contagion, extreme emotions influence and drive traders’ order execution behaviour. This study aims to empirically model the relationship between sentiment and trader aggressiveness for two types of traders: Whales and Retails. Additionally, I uncover the mediating roles of momentum, mean reversion, and market timing underpinning the dynamics of the off-chain Bitcoin market. Time series data collected from the Coinbase level 3 Bitcoin limit order book is first reconstructed to access its core structure. Next, sentiment data from Reddit is extracted and processed using an internal Natural Language Processing pipeline. These datasets are synthesized, and a suite of multivariate regression and autocorrelation models formulated to analyse the pertinent variables. Baseline results underscore the pivotal role of sentiment in significantly predicting Whale and Retail trader aggressiveness in the Bitcoin market. Mean reversion is evidenced by a negatively autocorrelating sentiment measure. Further regression analyses reveal the dynamics of the interplay of momentum, mean reversion, and market timing in predicting short-term and long-term sentiment. Whale order imbalance and Whale order aggressiveness are also found to be more effective in predicting Bitcoin returns. 2023-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/etd_coll/541 https://ink.library.smu.edu.sg/context/etd_coll/article/1539/viewcontent/GPBA_AY2019_DBA_AngeliqueNicoleThang.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Dissertations and Theses Collection (Open Access) eng Institutional Knowledge at Singapore Management University Bitcoin cryptocurrency limit order book market microstructure trader aggressiveness order aggressiveness sentiment trading. Finance Finance and Financial Management
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Bitcoin
cryptocurrency
limit order book
market microstructure
trader aggressiveness
order aggressiveness
sentiment
trading.
Finance
Finance and Financial Management
spellingShingle Bitcoin
cryptocurrency
limit order book
market microstructure
trader aggressiveness
order aggressiveness
sentiment
trading.
Finance
Finance and Financial Management
THANG, Angelique Nicole Huei Yin
Modelling sentiment and trader aggressiveness in cryptocurrency markets: An empirical analysis of the Bitcoin limit order book.
description Cryptocurrency assets are inherently susceptible to social sentiment. Due to emotional contagion, extreme emotions influence and drive traders’ order execution behaviour. This study aims to empirically model the relationship between sentiment and trader aggressiveness for two types of traders: Whales and Retails. Additionally, I uncover the mediating roles of momentum, mean reversion, and market timing underpinning the dynamics of the off-chain Bitcoin market. Time series data collected from the Coinbase level 3 Bitcoin limit order book is first reconstructed to access its core structure. Next, sentiment data from Reddit is extracted and processed using an internal Natural Language Processing pipeline. These datasets are synthesized, and a suite of multivariate regression and autocorrelation models formulated to analyse the pertinent variables. Baseline results underscore the pivotal role of sentiment in significantly predicting Whale and Retail trader aggressiveness in the Bitcoin market. Mean reversion is evidenced by a negatively autocorrelating sentiment measure. Further regression analyses reveal the dynamics of the interplay of momentum, mean reversion, and market timing in predicting short-term and long-term sentiment. Whale order imbalance and Whale order aggressiveness are also found to be more effective in predicting Bitcoin returns.
format text
author THANG, Angelique Nicole Huei Yin
author_facet THANG, Angelique Nicole Huei Yin
author_sort THANG, Angelique Nicole Huei Yin
title Modelling sentiment and trader aggressiveness in cryptocurrency markets: An empirical analysis of the Bitcoin limit order book.
title_short Modelling sentiment and trader aggressiveness in cryptocurrency markets: An empirical analysis of the Bitcoin limit order book.
title_full Modelling sentiment and trader aggressiveness in cryptocurrency markets: An empirical analysis of the Bitcoin limit order book.
title_fullStr Modelling sentiment and trader aggressiveness in cryptocurrency markets: An empirical analysis of the Bitcoin limit order book.
title_full_unstemmed Modelling sentiment and trader aggressiveness in cryptocurrency markets: An empirical analysis of the Bitcoin limit order book.
title_sort modelling sentiment and trader aggressiveness in cryptocurrency markets: an empirical analysis of the bitcoin limit order book.
publisher Institutional Knowledge at Singapore Management University
publishDate 2023
url https://ink.library.smu.edu.sg/etd_coll/541
https://ink.library.smu.edu.sg/context/etd_coll/article/1539/viewcontent/GPBA_AY2019_DBA_AngeliqueNicoleThang.pdf
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